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Claims-based algorithms are used in the Food and Drug Administration Sentinel Active Risk Identification and Analysis System to identify occurrences of health outcomes of interest (HOIs) for medical product safety assessment. This project aimed to apply machine learning classification techniques to demonstrate the feasibility of developing a claims-based algorithm to predict an HOI in structured electronic health record (EHR) data.

This study examined the perspectives on the use of data visualizations and identified key features seriously ill children, their parents, and clinicians prefer to see when visualizing symptom data obtained from mobile health technologies (an Apple Watch and smartphone symptom app).

The Global Digital Exemplar (GDE) Program is a national attempt to accelerate digital maturity in healthcare providers through promoting knowledge transfer across the English National Health Service (NHS). "Blueprints"-documents capturing implementation experience-were intended to facilitate this knowledge transfer. Here we explore how Blueprints have been conceptualized, produced, and used to promote interorganizational knowledge transfer across the NHS.

Facial masks are an essential personal protective measure to fight the COVID-19 (coronavirus disease) pandemic. However, the mask adoption rate in the United States is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies.

The objective of this study was to directly compare the ability of commonly used early warning scores (EWS) for early identification and prediction of sepsis in the general ward setting. For general ward patients at a large, academic medical center between early-2012 and mid-2018, common EWS and patient acuity scoring systems were calculated from electronic health records (EHR) data for patients that both met and did not meet Sepsis-3 criteria [...]

The effects of shared clinical notes on patients, care partners, and clinicians ("open notes") were first studied as a demonstration project in 2010. Since then, multiple studies have shown clinicians agree shared progress notes are beneficial to patients, and patients and care partners report benefits from reading notes. To determine if implementing open notes at a hematology/oncology practice changed providers' documentation style, we assessed the length and readability of clinicians' [...]

The widespread deployment of electronic health records (EHRs) has introduced new sources of error and inefficiencies to the process of ordering medications in the hospital setting. Existing work identifies orders that require pharmacy intervention by comparing them to a patient's medical records. In this work, we develop a machine learning model for identifying medication orders requiring intervention using only provider behavior and other contextual features that may reflect these new [...]

The COVID-19 pandemic and subsequent expansion of telehealth may be exacerbating inequities in ambulatory care access due to institutional and structural barriers. We conduct a repeat cross-sectional analysis of ambulatory patients to evaluate for demographic disparities in the utilization of telehealth modalities.

Disparities in adult patient portal adoption are well-documented; however, less is known about disparities in portal adoption in pediatrics. This study examines the prevalence and factors associated with patient portal activation and the use of specific portal features in general pediatrics.

We develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity (ZSS) methods are designed to exploit shared information between cancers and auxiliary class features, respectively, to boost performance using enriched annotations which give both location-based information and document level labels for each pathology report.